from selenium import webdriver
import time

from gen_captcha import gen_captcha_text_and_image
from gen_captcha import number
from gen_captcha import alphabet
from gen_captcha import ALPHABET

import cv2 as cv
import numpy as np
import tensorflow as tf

text, image = gen_captcha_text_and_image()
print("验证码图像channel:", image.shape)  # (60, 160, 3)
# 图像大小
IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160
MAX_CAPTCHA = len(text)
print("验证码文本最长字符数", MAX_CAPTCHA)   # 验证码最长4字符; 我全部固定为4,可以不固定. 如果验证码长度小于4，用'_'补齐

# 把彩色图像转为灰度图像（色彩对识别验证码没有什么用）
def convert2gray(img):
    if len(img.shape) > 2:
        gray = np.mean(img, -1)
        # 上面的转法较快，正规转法如下
        # r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
        # gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
        return gray
    else:
        return img

"""
cnn在图像大小是2的倍数时性能最高, 如果你用的图像大小不是2的倍数，可以在图像边缘补无用像素。
np.pad(image【,((2,3),(2,2)), 'constant', constant_values=(255,))  # 在图像上补2行，下补3行，左补2行，右补2行
"""

# 文本转向量
char_set = number + alphabet + ALPHABET + ['_']  # 如果验证码长度小于4, '_'用来补齐
CHAR_SET_LEN = len(char_set)
def text2vec(text):
    text_len = len(text)
    if text_len > MAX_CAPTCHA:
        raise ValueError('验证码最长4个字符')

    vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN) #生成一个默认为0的向量
    def char2pos(c):
        if c =='_':
            k = 62
            return k
        k = ord(c)-48
        if k > 9:
            k = ord(c) - 55
            if k > 35:
                k = ord(c) - 61
                if k > 61:
                    raise ValueError('No Map')
        return k
    for i, c in enumerate(text):
        idx = i * CHAR_SET_LEN + char2pos(c)
        vector[idx] = 1
    return vector
# 向量转回文本
def vec2text(vec):
    char_pos = vec.nonzero()[0]
    text=[]
    for i, c in enumerate(char_pos):
        char_at_pos = i #c/63
        char_idx = c % CHAR_SET_LEN
        if char_idx < 10:
            char_code = char_idx + ord('0')
        elif char_idx <36:
            char_code = char_idx - 10 + ord('A')
        elif char_idx < 62:
            char_code = char_idx-  36 + ord('a')
        elif char_idx == 62:
            char_code = ord('_')
        else:
            raise ValueError('error')
        text.append(chr(char_code))
    return "".join(text)

"""
#向量（大小MAX_CAPTCHA*CHAR_SET_LEN）用0,1编码 每63个编码一个字符，这样顺利有，字符也有
vec = text2vec("F5Sd")
text = vec2text(vec)
print(text)  # F5Sd
vec = text2vec("SFd5")
text = vec2text(vec)
print(text)  # SFd5
"""

# 生成一个训练batchv  一个批次为 默认128 张图片 转换为向量
def get_next_batch(batch_size=128):
    batch_x = np.zeros([batch_size, IMAGE_HEIGHT*IMAGE_WIDTH])
    batch_y = np.zeros([batch_size, MAX_CAPTCHA*CHAR_SET_LEN])

    # 有时生成图像大小不是(60, 160, 3)  反复获取验证码直到该验证码符合标准格式。
    def wrap_gen_captcha_text_and_image():
        while True:
            text, image = gen_captcha_text_and_image()
            if image.shape == (60, 160, 3):
                return text, image

    for i in range(batch_size):
        #获取图片，并灰度转换
        text, image = wrap_gen_captcha_text_and_image()
        image = convert2gray(image)

        # flatten 图片一维化 以及对应的文字内容也一维化，形成一个128行每行一个图片及对应文本
        batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128  mean为0
        batch_y[i,:] = text2vec(text)

    return batch_x, batch_y

####################################################################

# 申请三个占位符
X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT*IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA*CHAR_SET_LEN])
keep_prob = tf.placeholder(tf.float32) # dropout

# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
    x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])

    #w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) #
    #w_c2_alpha = np.sqrt(2.0/(3*3*32))
    #w_c3_alpha = np.sqrt(2.0/(3*3*64))
    #w_d1_alpha = np.sqrt(2.0/(8*32*64))
    #out_alpha = np.sqrt(2.0/1024)

    # 3 conv layer # 3 个 转换层
    w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 1, 32]))
    b_c1 = tf.Variable(b_alpha*tf.random_normal([32]))
    conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    conv1 = tf.nn.dropout(conv1, keep_prob)

    w_c2 = tf.Variable(w_alpha*tf.random_normal([3, 3, 32, 64]))
    b_c2 = tf.Variable(b_alpha*tf.random_normal([64]))
    conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    conv2 = tf.nn.dropout(conv2, keep_prob)

    w_c3 = tf.Variable(w_alpha*tf.random_normal([3, 3, 64, 64]))
    b_c3 = tf.Variable(b_alpha*tf.random_normal([64]))
    conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
    conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    conv3 = tf.nn.dropout(conv3, keep_prob)

    # Fully connected layer  # 最后连接层
    w_d = tf.Variable(w_alpha*tf.random_normal([8*20*64, 1024]))
    b_d = tf.Variable(b_alpha*tf.random_normal([1024]))
    dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
    dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
    dense = tf.nn.dropout(dense, keep_prob)

    # 输出层
    w_out = tf.Variable(w_alpha*tf.random_normal([1024, MAX_CAPTCHA*CHAR_SET_LEN]))
    b_out = tf.Variable(b_alpha*tf.random_normal([MAX_CAPTCHA*CHAR_SET_LEN]))
    out = tf.add(tf.matmul(dense, w_out), b_out)
    #out = tf.nn.softmax(out)
    return out

# 训练
def train_crack_captcha_cnn():
    output = crack_captcha_cnn()
    # loss 损失数值
    # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))
    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
        # 最后一层用来分类的softmax和sigmoid有什么不同？
    tf.summary.scalar('loss',loss)
    # optimizer 为了加快训练 learning_rate 应该开始大，然后慢慢衰
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

    predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
    max_idx_p = tf.argmax(predict, 2)
    max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
    correct_pred = tf.equal(max_idx_p, max_idx_l)
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
    tf.summary.scalar('accuracy',accuracy)

    merged = tf.summary.merge_all()
    saver = tf.train.Saver()
    with tf.Session() as sess:
        train_writer = tf.summary.FileWriter('./train',sess.graph)
        #test_writer = tf.summary.FileWriter('./test')
        sess.run(tf.global_variables_initializer())

        step = 0
        while True:
            batch_x, batch_y = get_next_batch(64)
            _, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
            print(step, loss_)
            curve = sess.run(merged, feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
            train_writer.add_summary(curve,step)

            # 每100 step计算一次准确率
            if step % 100 == 0:
                batch_x_test, batch_y_test = get_next_batch(100)
                summary,acc = sess.run([merged,accuracy], feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
                print(step, acc)
                train_writer.add_summary(summary,step)
                # 如果准确率大于50%,保存模型,完成训练
                if acc > 0.9:
                    saver.save(sess, "./crack_capcha.model", global_step=step)
                    break
            step += 1
        train_writer.close()
        saver.save(sess, "./crack_capcha.model", global_step=step)

def crack_captcha(captcha_image):
    output = crack_captcha_cnn()

    saver = tf.train.Saver()
    with tf.Session() as sess:
        saver.restore(sess, tf.train.latest_checkpoint('.'))

        predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
        text_list = sess.run(predict, feed_dict={X: [[captcha_image]], keep_prob: [1]})

        text = text_list[0].tolist()
        vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
        i = 0
        for n in text:
                vector[i*CHAR_SET_LEN + n] = 1
                i += 1
        return vec2text(vector)

# -------------------------------------------------------------------------------------

browser = webdriver.Chrome()
browser.get('http://jwxt1.cumt.edu.cn/')
browser.maximize_window()

input = browser.find_element_by_id('txtUserName')#找到搜索框
input.send_keys('08143106')#传送入关键词
input = browser.find_element_by_id('TextBox2')#找到搜索框
input.send_keys('pipi686712')#传送入关键词

# img_src = browser.find_element_by_id('icode')#找到搜索框

#img_src = int(img_src)

# import requests as req
# from PIL import Image
# from io import BytesIO
# response = req.get('http://jwxt1.cumt.edu.cn/CheckCode.aspx')
# image = Image.open(BytesIO(response.content))
# image.show()



# import cv2 as cv
# cap=cv.VideoCapture('http://jwxt1.cumt.edu.cn/CheckCode.aspx')
#
# if( cap.isOpened() ) :
#     ret,img = cap.read()
#     print(img.shape)
#     res = cv.resize(img, (160, 60), interpolation=cv.INTER_CUBIC)
#     cv.imshow('iker', res)
#     cv.imshow('image', img)
#     print(res.shape)
#     cv.waitKey(0)
#     cv.destoryWindows()



img_src = 'http://jwxt1.cumt.edu.cn/CheckCode.aspx'
cap = cv.VideoCapture(img_src)
if( cap.isOpened() ) :
    ret,img = cap.read()

    print(img.shape)
    res=cv.resize(img,(160,60),interpolation=cv.INTER_CUBIC)
    cv.startWindowThread()  # 加在这个位置
    cv.imshow('image', img)
    cv.imshow('res', res)
    print(res.shape)
    cv.waitKey(0)
    #cv.destoryWindows()

    image = convert2gray(res)  # 生成一张新图
    image = image.flatten() / 255  # 将图片一维化
    #image = tf.reshape(image, [-1, 9600])
    #image = image.reshape([-1,9600])
    predict_text = crack_captcha(image)  # 导入模型识别
    print("预测: {}".format(predict_text))

    input = browser.find_element_by_id('txtSecretCode')  # 找到搜索框
    input.send_keys('jfid')  # 传送入关键词

    #time.sleep(3)
    button = browser.find_element_by_id('Button1')#找到搜索按钮
    button.click()